IDAP BLOG Artificial Intelligence

Curious AI & ML Trends 2020


2020 is just around the corner, and we can’t believe it’s been another year so fast. This year happened to be a fruitful one for the world of technology! Especially that accounts for Artificial Intelligence and Machine Learning field, which can be easily proclaimed the it tech of the year, and probably of the whole decade. That alone makes us wondering, what’s there in 2020 awaiting for AI & ML the industry? Here are our top five picks for what’s going to be trending in AI and ML next year.

# 1 AI & ML Spreading

Businesses adopt AI-driven applications on a massive scale. The IT budgets are growing, non-tech employers are increasing their computer literacy, and top managers are becoming more and more open for the Machine Learning stack as a step towards business automation. From being uncertain, if not to say risky, AI and ML initiatives turned out to be the most powerful tool for business analysis, risk assessment, and budget optimization. 

In one of its recent surveys, Gartner has reported the number of companies investing in AI enterprise solutions raised from 4% in 2018 to 14% in 2019. After the world has seen the actual results of AI & ML technologies applied to business issues, such growth is a naturally occurring situation that doesn’t seem to change in 2020.

#2 AI and ML Literacy

Although Artificial Intelligence as a standalone technology isn’t enigmatic for anyone these days, people still aren’t much educated when it comes to real-life AI applications and companies developing them. If this occurs in non-tech people or those who simply uninterested in the topic, it doesn’t really matter, to be honest. But for those who work or especially invest in the IT industry, lack of education may lead to fatal decisions. 

A shocking example of people being unaware of what they’re investing in is reported by MMC Ventures’ The State of AI 2019: Divergence Report. Namely, the agency analyzed more than 2,800 companies that define themselves as AI software development ones and concluded that only about 60% of them actually bring AI and/or ML value to the market. The rest 40% rely on some other technologies with Artificial Intelligence being an addition to the stack, if found at all in the company’s products and operations. If you or someone you know are considering AI startup investment, please make sure to consult with proven field experts before signing the deal. With its spreading, Machine Learning and other AI subsets have also become more understandable and visual even for inexperienced learners. 

#3 ML Impacts Labor Market

What we mean by practice here, is AI-driven application to real-life business issues like cybersecurity, customer behavior predicting, fraud detection, etc. In 2020, the artificial stack will become more down-to-earth due to its accessibility to employers who will work with ML and DL products on-hand. This situation comes as a natural consequence of Machine Learning popularity overlapping with growing budgets and an increased number of active ML practitioners. 

In terms of soon implementation, according to Gartner, 37% of tech companies admitted to being in the process of Machine Learning deployment or planning it within the nearest time. Cybersecurity tops the list of practical AI use cases, followed by digital assistants, analytics — which by the way deserves to have its separate moment — and banking operations amongst many.  In its turn, this means a major shift in the labor market. No, this doesn’t mean AI will take over our jobs and eat our lunch, unlike somewhat popular opinion. Vice versa, penetration of AI into basically all spheres will generate new jobs and increase the level of tech skills in some of the already existing ones. In particular, U.S. Bureau of Labor Statistics has indicated 11.5 million Data Science job openings will be created by 2026. Mind that the area of Data Science in terms of career includes numerous positions, from data entry manager, statistician, and data scientist to enterprise architect, ML engineer, data & infrastructure architect. 

#4 AI as a Service

The traditional software-as-a-service (SaaS) direction has proven its effectiveness decades ago and continues to save huge amounts of money to companies that need software solutions but cannot go for in-house development. In 2020, as-a-service marketplace will emerge around AI solutions and their accessibility to companies with no AI scientists employed. Of course, large corporations will develop inside AI & ML departments just to avoid high dependence on third-party providers but keeping the fundamental AI capabilities close and easily manageable. However, small- and mid-size companies would rather choose AIaaS targeted to solve some specific tasks. 

Beyond question, to partner up with a proven Machine Learning engineering team is way easier (and cheaper) than to start building a network from square one. Oftentimes, it’s the only logical and sense-bearing way to go, e. g., an AI needed for a specific business operation, single-time application, or limited in time function. Prebuilt digital assistants, AI microservices, and other artificially smart digital products will be on the rise in 2020, which comes hand-in-hand with higher AI accessibility, proliferation of AI startups, and enormous demand for ML jobs on the market. However, do not expect AIaaS to replace enterprise-wide AI strategy on a large scale. In order to benefit from Machine Learning plugged in companies’ operations, offices still need to do plenty of research, raise their tech skills, think through the ML use strategy, and shape their vision of Machine Learning incorporated in their products and/or services.

#5 Augmented Everything

Augmented analytics is a practice of applying Machine Learning to data management. And it will continue to strengthen its positions in 2020 like we’ve never seen before! As in case with other trends on our list, the rise of augmented solutions for data management is a natural after-effect of people realizing what results it brings and what wonders it makes to companies’ performance in customers’ eyes. 

In 2020, companies will get even more insights from the gathered data, and those who haven’t started their ML analytics journey yet, will jump on that train inspired by their fellow entities. However, this doesn’t mean augmented analytics will be just as in used to be in 2019. The upcoming year is promising to bring its changes, like democratization of data collecting and processing, higher transparency, careful attitude towards user privacy, and maximized analysis results with minimum technical expertise applied. SaaS market giants like Salesforce and Creatio (previously known as BPM’online) are extending the range of augmented analytics functions in their products to deliver better experience to their clients as well. A few years ago, Gartner predicted 40% of all data management tasks will be automated by 2020, most of them — due to the augmented data operations functionality. We’ll see in a year!


Machine Learning as a subset and Artificial Intelligence as the whole area of science are topics of unlimited potential and exponentially growing public interest. And even with all the mindblowing capabilities of AI we are witnessing today, it seems to us that’s just a start and greater things are yet to come for that field. We hope this article has intrigued you to welcome the 2020 and see what in brings to the technology table. As always, if you want to forward us any questions or thoughts — click here and initiate a discussion.

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